import torch
from .models.DeepLab_v3 import DeepLab_v3
from PIL import Image
from torchvision import transforms

def detect(filename):

    model = DeepLab_v3(pretrain=True)
    input_image = Image.open(filename)
    preprocess = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    input_tensor = preprocess(input_image)
    input_batch = input_tensor.unsqueeze(0)  # create a mini-batch as expected by the model

    # move the input and model to GPU for speed if available
    if torch.cuda.is_available():
        input_batch = input_batch.to('cuda')
        model.to('cuda')

    with torch.no_grad():
        output = model(input_batch)['out'][0]
    output_predictions = output.argmax(0)
    # # create a color pallette, selecting a color for each class
    # palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
    # colors = torch.as_tensor([i for i in range(21)])[:, None] * palette
    # colors = (colors % 255).numpy().astype("uint8")
    #
    # # plot the semantic segmentation predictions of 21 classes in each color
    # r = Image.fromarray(output_predictions.byte().cpu().numpy()).resize(input_image.size)
    # r.putpalette(colors)
    #
    # import matplotlib.pyplot as plt
    # plt.imshow(r)
    # plt.show()
    return output_predictions
if __name__ == '__main__':
    detect(filename='170927_063941090_Camera_6.jpg')